Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 356

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Climate-smart agriculture: what is it good for?

Taylor, Marcus·2018·The Journal of Peasant Studies

Abstract

Leading international organisations presently argue that a transition to ‘climate-smart agriculture’ (CSA) is an obligatory task to ensure food supply for an anticipated nine billion people by 2050. Despite the rubric’s newfound importance, the conceptual underpinnings of CSA are often left unclear. Focusing on the World Bank’s framework, this paper critically interrogates the principles and concepts that underpin CSA. It argues that while CSA provides greater policy space for more holistic approaches to agriculture, it nonetheless operates within an apolitical framework that is narrowly focused on technical fixes at the level of production. This depoliticised approach to the global food system tends to validate existing policy agendas and minimise questions concerning power, inequality and access. By highlighting four strong tensions that permeate the CSA framework, the paper extols the need to greatly widen the scope of debate. To this end, it proposes an alternative ‘climate-wise’ framework to foreground the inherently political dimensions of food and agriculture in an era of climatic change.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1080/03066150.2017.1312355

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 2 references

Cited by

See all 356

Metadata

Authors
Taylor, M.
Venue
The Journal of Peasant Studies
Published
2017-05-25
Volume
45
Issue
1
Pages
89-107
ISSN
  • 0306-6150 (ISSN-L)
  • 1743-9361
OpenAlex
W2620475968
Cited by
356
References
2
FWCI
49.52
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Climate-smart agriculture: what is it good for? https://app.cochat.ai/papers/10.1080/03066150.2017.1312355
The paper
Taylor, M. (2018). Climate-smart agriculture: what is it good for? The Journal of Peasant Studies. https://doi.org/10.1080/03066150.2017.1312355

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.